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André Anjos authoredAndré Anjos authored
test_jfa.py 11.38 KiB
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
# Wed Feb 15 23:24:35 2012 +0200
#
# Copyright (C) 2011-2013 Idiap Research Institute, Martigny, Switzerland
"""Tests on the JFA-based machines
"""
import os, sys
import unittest
import math
import bob
import numpy, numpy.linalg
import tempfile
def estimate_x(dim_c, dim_d, mean, sigma, U, N, F):
# Compute helper values
UtSigmaInv = {}
UtSigmaInvU = {}
dim_ru = U.shape[1]
for c in range(dim_c):
start = c*dim_d
end = (c+1)*dim_d
Uc = U[start:end,:]
UtSigmaInv[c] = Uc.transpose() / sigma[start:end]
UtSigmaInvU[c] = numpy.dot(UtSigmaInv[c], Uc);
# I + (U^{T} \Sigma^-1 N U)
I_UtSigmaInvNU = numpy.eye(dim_ru, dtype=numpy.float64)
for c in range(dim_c):
I_UtSigmaInvNU = I_UtSigmaInvNU + UtSigmaInvU[c] * N[c]
# U^{T} \Sigma^-1 F
UtSigmaInv_Fnorm = numpy.zeros((dim_ru,), numpy.float64)
for c in range(dim_c):
start = c*dim_d
end = (c+1)*dim_d
Fnorm = F[c,:] - N[c] * mean[start:end]
UtSigmaInv_Fnorm = UtSigmaInv_Fnorm + numpy.dot(UtSigmaInv[c], Fnorm)
return numpy.linalg.solve(I_UtSigmaInvNU, UtSigmaInv_Fnorm)
def estimate_ux(dim_c, dim_d, mean, sigma, U, N, F):
return numpy.dot(U, estimate_x(dim_c, dim_d, mean, sigma, U, N, F))
class FATest(unittest.TestCase):
"""Performs various FA tests."""
def test01_JFABase(self):
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = bob.machine.GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a JFABase
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
V = numpy.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
m = bob.machine.JFABase(ubm)
self.assertTrue( m.dim_ru == 1)
self.assertTrue( m.dim_rv == 1)
# Checks for correctness
m.resize(2,2)
m.u = U
m.v = V
m.d = d
self.assertTrue( (m.u == U).all() )
self.assertTrue( (m.v == V).all() )
self.assertTrue( (m.d == d).all() )
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
self.assertTrue( m.dim_rv == 2)
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.HDF5File(filename, 'w'))
m_loaded = bob.machine.JFABase(bob.io.HDF5File(filename))
m_loaded.ubm = ubm
self.assertTrue( m == m_loaded )
self.assertFalse( m != m_loaded )
self.assertTrue( m.is_similar_to(m_loaded) )
# Copy constructor
mc = bob.machine.JFABase(m)
self.assertTrue( m == mc )
# Variant
mv = bob.machine.JFABase()
# Checks for correctness
mv.ubm = ubm
mv.resize(2,2)
mv.u = U
mv.v = V
mv.d = d
self.assertTrue( (m.u == U).all() )
self.assertTrue( (m.v == V).all() )
self.assertTrue( (m.d == d).all() )
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
self.assertTrue( m.dim_rv == 2)
# Clean-up
os.unlink(filename)
def test02_ISVBase(self):
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = bob.machine.GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a ISVBase
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
m = bob.machine.ISVBase(ubm)
self.assertTrue( m.dim_ru == 1)
# Checks for correctness
m.resize(2)
m.u = U
m.d = d
self.assertTrue( (m.u == U).all() )
self.assertTrue( (m.d == d).all() )
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.HDF5File(filename, 'w'))
m_loaded = bob.machine.ISVBase(bob.io.HDF5File(filename))
m_loaded.ubm = ubm
self.assertTrue( m == m_loaded )
self.assertFalse( m != m_loaded )
self.assertTrue( m.is_similar_to(m_loaded) )
# Copy constructor
mc = bob.machine.ISVBase(m)
self.assertTrue( m == mc )
# Variant
mv = bob.machine.ISVBase()
# Checks for correctness
mv.ubm = ubm
mv.resize(2)
mv.u = U
mv.d = d
self.assertTrue( (m.u == U).all() )
self.assertTrue( (m.d == d).all() )
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
# Clean-up
os.unlink(filename)
def test03_JFAMachine(self):
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = bob.machine.GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a JFABase
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
V = numpy.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
base = bob.machine.JFABase(ubm,2,2)
base.u = U
base.v = V
base.d = d
# Creates a JFAMachine
y = numpy.array([1,2], 'float64')
z = numpy.array([3,4,1,2,0,1], 'float64')
m = bob.machine.JFAMachine(base)
m.y = y
m.z = z
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
self.assertTrue( m.dim_rv == 2)
self.assertTrue( (m.y == y).all() )
self.assertTrue( (m.z == z).all() )
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.HDF5File(filename, 'w'))
m_loaded = bob.machine.JFAMachine(bob.io.HDF5File(filename))
m_loaded.jfa_base = base
self.assertTrue( m == m_loaded )
self.assertFalse( m != m_loaded )
self.assertTrue(m.is_similar_to(m_loaded))
# Copy constructor
mc = bob.machine.JFAMachine(m)
self.assertTrue( m == mc )
# Variant
mv = bob.machine.JFAMachine()
# Checks for correctness
mv.jfa_base = base
m.y = y
m.z = z
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
self.assertTrue( m.dim_rv == 2)
self.assertTrue( (m.y == y).all() )
self.assertTrue( (m.z == z).all() )
# Defines GMMStats
gs = bob.machine.GMMStats(2,3)
log_likelihood = -3.
T = 1
n = numpy.array([0.4, 0.6], 'float64')
sumpx = numpy.array([[1., 2., 3.], [4., 5., 6.]], 'float64')
sumpxx = numpy.array([[10., 20., 30.], [40., 50., 60.]], 'float64')
gs.log_likelihood = log_likelihood
gs.t = T
gs.n = n
gs.sum_px = sumpx
gs.sum_pxx = sumpxx
# Forward GMMStats and check estimated value of the x speaker factor
eps = 1e-10
x_ref = numpy.array([0.291042849767692, 0.310273618998444], 'float64')
score_ref = -2.111577181208289
score = m.forward(gs)
self.assertTrue( numpy.allclose(m.__x__, x_ref, eps) )
self.assertTrue( abs(score_ref-score) < eps )
# x and Ux
x = numpy.ndarray((2,), numpy.float64)
m.estimate_x(gs, x)
x_py = estimate_x(m.dim_c, m.dim_d, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
self.assertTrue( numpy.allclose(x, x_py, eps))
ux = numpy.ndarray((6,), numpy.float64)
m.estimate_ux(gs, ux)
ux_py = estimate_ux(m.dim_c, m.dim_d, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
self.assertTrue( numpy.allclose(ux, ux_py, eps))
self.assertTrue( numpy.allclose(m.__x__, x, eps) )
score = m.forward_ux(gs, ux)
self.assertTrue( abs(score_ref-score) < eps )
# Clean-up
os.unlink(filename)
def test04_ISVMachine(self):
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = bob.machine.GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a JFABaseMachine
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
V = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
base = bob.machine.ISVBase(ubm,2)
base.u = U
base.v = V
base.d = d
# Creates a JFAMachine
z = numpy.array([3,4,1,2,0,1], 'float64')
m = bob.machine.ISVMachine(base)
m.z = z
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
self.assertTrue( (m.z == z).all() )
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.HDF5File(filename, 'w'))
m_loaded = bob.machine.ISVMachine(bob.io.HDF5File(filename))
m_loaded.isv_base = base
self.assertTrue( m == m_loaded )
self.assertFalse( m != m_loaded )
self.assertTrue(m.is_similar_to(m_loaded))
# Copy constructor
mc = bob.machine.ISVMachine(m)
self.assertTrue( m == mc )
# Variant
mv = bob.machine.ISVMachine()
# Checks for correctness
mv.isv_base = base
m.z = z
self.assertTrue( m.dim_c == 2)
self.assertTrue( m.dim_d == 3)
self.assertTrue( m.dim_cd == 6)
self.assertTrue( m.dim_ru == 2)
self.assertTrue( (m.z == z).all() )
# Defines GMMStats
gs = bob.machine.GMMStats(2,3)
log_likelihood = -3.
T = 1
n = numpy.array([0.4, 0.6], 'float64')
sumpx = numpy.array([[1., 2., 3.], [4., 5., 6.]], 'float64')
sumpxx = numpy.array([[10., 20., 30.], [40., 50., 60.]], 'float64')
gs.log_likelihood = log_likelihood
gs.t = T
gs.n = n
gs.sum_px = sumpx
gs.sum_pxx = sumpxx
# Forward GMMStats and check estimated value of the x speaker factor
eps = 1e-10
x_ref = numpy.array([0.291042849767692, 0.310273618998444], 'float64')
score_ref = -3.280498193082100
score = m.forward(gs)
self.assertTrue( numpy.allclose(m.__x__, x_ref, eps) )
self.assertTrue( abs(score_ref-score) < eps )
# Check using alternate forward() method
Ux = numpy.ndarray(shape=(m.dim_cd,), dtype=numpy.float64)
m.estimate_ux(gs, Ux)
score = m.forward_ux(gs, Ux)
self.assertTrue( abs(score_ref-score) < eps )
# x and Ux
x = numpy.ndarray((2,), numpy.float64)
m.estimate_x(gs, x)
x_py = estimate_x(m.dim_c, m.dim_d, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
self.assertTrue( numpy.allclose(x, x_py, eps))
ux = numpy.ndarray((6,), numpy.float64)
m.estimate_ux(gs, ux)
ux_py = estimate_ux(m.dim_c, m.dim_d, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
self.assertTrue( numpy.allclose(ux, ux_py, eps))
self.assertTrue( numpy.allclose(m.__x__, x, eps) )
score = m.forward_ux(gs, ux)
self.assertTrue( abs(score_ref-score) < eps )
# Clean-up
os.unlink(filename)